{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResnetTrick_s128_e200\n",
    "\n",
    "> size 128 bs 64 200 epochs runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# setup and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/ayasyrev/model_constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/kornia/kornia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kornia.contrib import MaxBlurPool2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.basic_train import *\n",
    "from fastai.vision import *\n",
    "from fastai.script import *\n",
    "from model_constructor.net import *\n",
    "from model_constructor.layers import SimpleSelfAttention, ConvLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ranger deep learning optimizer - RAdam + Lookahead combined.\n",
    "  #https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer\n",
    "\n",
    "  #Ranger has now been used to capture 12 records on the FastAI leaderboard.\n",
    "\n",
    "  #This version = 9.3.19  \n",
    "\n",
    "  #Credits:\n",
    "  #RAdam -->  https://github.com/LiyuanLucasLiu/RAdam\n",
    "  #Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.\n",
    "  #Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610\n",
    "\n",
    "  #summary of changes: \n",
    "  #full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), \n",
    "  #supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.\n",
    "  #changes 8/31/19 - fix references to *self*.N_sma_threshold; \n",
    "                  #changed eps to 1e-5 as better default than 1e-8.\n",
    "\n",
    "import math\n",
    "import torch\n",
    "from torch.optim.optimizer import Optimizer, required\n",
    "import itertools as it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mish(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        print(\"Mish activation loaded...\")\n",
    "\n",
    "    def forward(self, x):  \n",
    "        #save 1 second per epoch with no x= x*() and then return x...just inline it.\n",
    "        return x *( torch.tanh(F.softplus(x))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Ranger(Optimizer):\n",
    "\n",
    "    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95,0.999), eps=1e-5, weight_decay=0):\n",
    "        #parameter checks\n",
    "        if not 0.0 <= alpha <= 1.0:\n",
    "            raise ValueError(f'Invalid slow update rate: {alpha}')\n",
    "        if not 1 <= k:\n",
    "            raise ValueError(f'Invalid lookahead steps: {k}')\n",
    "        if not lr > 0:\n",
    "            raise ValueError(f'Invalid Learning Rate: {lr}')\n",
    "        if not eps > 0:\n",
    "            raise ValueError(f'Invalid eps: {eps}')\n",
    "\n",
    "        #parameter comments:\n",
    "        # beta1 (momentum) of .95 seems to work better than .90...\n",
    "        #N_sma_threshold of 5 seems better in testing than 4.\n",
    "        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.\n",
    "\n",
    "        #prep defaults and init torch.optim base\n",
    "        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)\n",
    "        super().__init__(params,defaults)\n",
    "\n",
    "        #adjustable threshold\n",
    "        self.N_sma_threshhold = N_sma_threshhold\n",
    "\n",
    "        #now we can get to work...\n",
    "        #removed as we now use step from RAdam...no need for duplicate step counting\n",
    "        #for group in self.param_groups:\n",
    "        #    group[\"step_counter\"] = 0\n",
    "            #print(\"group step counter init\")\n",
    "\n",
    "        #look ahead params\n",
    "        self.alpha = alpha\n",
    "        self.k = k \n",
    "\n",
    "        #radam buffer for state\n",
    "        self.radam_buffer = [[None,None,None] for ind in range(10)]\n",
    "\n",
    "        #self.first_run_check=0\n",
    "\n",
    "        #lookahead weights\n",
    "        #9/2/19 - lookahead param tensors have been moved to state storage.  \n",
    "        #This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.\n",
    "\n",
    "        #self.slow_weights = [[p.clone().detach() for p in group['params']]\n",
    "        #                     for group in self.param_groups]\n",
    "\n",
    "        #don't use grad for lookahead weights\n",
    "        #for w in it.chain(*self.slow_weights):\n",
    "        #    w.requires_grad = False\n",
    "\n",
    "    def __setstate__(self, state):\n",
    "        print(\"set state called\")\n",
    "        super(Ranger, self).__setstate__(state)\n",
    "\n",
    "\n",
    "    def step(self, closure=None):\n",
    "        loss = None\n",
    "        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.  \n",
    "        #Uncomment if you need to use the actual closure...\n",
    "\n",
    "        #if closure is not None:\n",
    "            #loss = closure()\n",
    "\n",
    "        #Evaluate averages and grad, update param tensors\n",
    "        for group in self.param_groups:\n",
    "\n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                grad = p.grad.data.float()\n",
    "                if grad.is_sparse:\n",
    "                    raise RuntimeError('Ranger optimizer does not support sparse gradients')\n",
    "\n",
    "                p_data_fp32 = p.data.float()\n",
    "\n",
    "                state = self.state[p]  #get state dict for this param\n",
    "\n",
    "                if len(state) == 0:   #if first time to run...init dictionary with our desired entries\n",
    "                    #if self.first_run_check==0:\n",
    "                        #self.first_run_check=1\n",
    "                        #print(\"Initializing slow buffer...should not see this at load from saved model!\")\n",
    "                    state['step'] = 0\n",
    "                    state['exp_avg'] = torch.zeros_like(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n",
    "\n",
    "                    #look ahead weight storage now in state dict \n",
    "                    state['slow_buffer'] = torch.empty_like(p.data)\n",
    "                    state['slow_buffer'].copy_(p.data)\n",
    "\n",
    "                else:\n",
    "                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n",
    "\n",
    "                #begin computations \n",
    "                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n",
    "                beta1, beta2 = group['betas']\n",
    "\n",
    "                #compute variance mov avg\n",
    "                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n",
    "                #compute mean moving avg\n",
    "                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n",
    "\n",
    "                state['step'] += 1\n",
    "\n",
    "\n",
    "                buffered = self.radam_buffer[int(state['step'] % 10)]\n",
    "                if state['step'] == buffered[0]:\n",
    "                    N_sma, step_size = buffered[1], buffered[2]\n",
    "                else:\n",
    "                    buffered[0] = state['step']\n",
    "                    beta2_t = beta2 ** state['step']\n",
    "                    N_sma_max = 2 / (1 - beta2) - 1\n",
    "                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)\n",
    "                    buffered[1] = N_sma\n",
    "                    if N_sma > self.N_sma_threshhold:\n",
    "                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])\n",
    "                    else:\n",
    "                        step_size = 1.0 / (1 - beta1 ** state['step'])\n",
    "                    buffered[2] = step_size\n",
    "\n",
    "                if group['weight_decay'] != 0:\n",
    "                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)\n",
    "\n",
    "                if N_sma > self.N_sma_threshhold:\n",
    "                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n",
    "                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)\n",
    "                else:\n",
    "                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)\n",
    "\n",
    "                p.data.copy_(p_data_fp32)\n",
    "\n",
    "                #integrated look ahead...\n",
    "                #we do it at the param level instead of group level\n",
    "                if state['step'] % group['k'] == 0:\n",
    "                    slow_p = state['slow_buffer'] #get access to slow param tensor\n",
    "                    slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha\n",
    "                    p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(size=128, woof=1, bs=64, workers=None, **kwargs):\n",
    "    if woof:\n",
    "        path = URLs.IMAGEWOOF    # if woof \n",
    "    else:\n",
    "        path = URLs.IMAGENETTE\n",
    "    path = untar_data(path)\n",
    "    print('data path  ', path)\n",
    "    n_gpus = num_distrib() or 1\n",
    "    if workers is None: workers = min(8, num_cpus()//n_gpus)\n",
    "    return (ImageList.from_folder(path).split_by_folder(valid='val')\n",
    "            .label_from_folder().transform(([flip_lr(p=0.5)], []), size=size)\n",
    "            .databunch(bs=bs, num_workers=workers)\n",
    "            .presize(size, scale=(0.35,1))\n",
    "            .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_learn(\n",
    "        gpu:Param(\"GPU to run on\", str)=None,\n",
    "        woof: Param(\"Use imagewoof (otherwise imagenette)\", int)=1,\n",
    "        size: Param(\"Size (px: 128,192,224)\", int)=128,\n",
    "        alpha: Param(\"Alpha\", float)=0.99, \n",
    "        mom: Param(\"Momentum\", float)=0.95, #? 0.9\n",
    "        eps: Param(\"epsilon\", float)=1e-6,\n",
    "        bs: Param(\"Batch size\", int)=64,\n",
    "        mixup: Param(\"Mixup\", float)=0.,\n",
    "        opt: Param(\"Optimizer (adam,rms,sgd)\", str)='ranger',\n",
    "        sa: Param(\"Self-attention\", int)=0,\n",
    "        sym: Param(\"Symmetry for self-attention\", int)=0,\n",
    "        model: Param('model as partial', callable) = xresnet50\n",
    "        ):\n",
    " \n",
    "    if   opt=='adam' : opt_func = partial(optim.Adam, betas=(mom,alpha), eps=eps)\n",
    "    elif opt=='ranger'  : opt_func = partial(Ranger,  betas=(mom,alpha), eps=eps)\n",
    "    data = get_data(size, woof, bs)\n",
    "    learn = (Learner(data, model(), wd=1e-2, opt_func=opt_func,\n",
    "             metrics=[accuracy,top_k_accuracy],\n",
    "             bn_wd=False, true_wd=True,\n",
    "             loss_func = LabelSmoothingCrossEntropy(),))\n",
    "    print('Learn path', learn.path)\n",
    "    if mixup: learn = learn.mixup(alpha=mixup)\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(learn):\n",
    "    learn.recorder.plot_losses()\n",
    "    learn.recorder.plot_metrics()\n",
    "    learn.recorder.plot_lr(show_moms=True)\n",
    "Learner.plot = plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.004\n",
    "epochs = 200\n",
    "moms = (0.95,0.95)\n",
    "start_pct = 0.4\n",
    "size=128\n",
    "bs=64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewResBlock(Module):\n",
    "    def __init__(self, expansion, ni, nh, stride=1, \n",
    "                 conv_layer=ConvLayer, act_fn=act_fn, bn_1st=True,\n",
    "                 pool=nn.AvgPool2d(2, ceil_mode=True), sa=False,sym=False, zero_bn=True):\n",
    "        nf,ni = nh*expansion,ni*expansion\n",
    "        self.reduce = noop if stride==1 else pool\n",
    "        layers  = [(f\"conv_0\", conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\", conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ] if expansion == 1 else [\n",
    "                   (f\"conv_0\",conv_layer(ni, nh, 1, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\",conv_layer(nh, nh, 3, stride=1, act_fn=act_fn, bn_1st=bn_1st)), #!!!\n",
    "                   (f\"conv_2\",conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ]\n",
    "        if sa: layers.append(('sa', SimpleSelfAttention(nf,ks=1,sym=sym)))\n",
    "        self.convs = nn.Sequential(OrderedDict(layers))\n",
    "        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)\n",
    "        self.merge =act_fn\n",
    "\n",
    "    def forward(self, x): \n",
    "        o = self.reduce(x)\n",
    "        return self.merge(self.convs(o) + self.idconv(o))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xresnet50(c_out=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.block = NewResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = MaxBlurPool2d(3, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.pool = pool\n",
    "model.stem_pool = pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mish activation loaded...\n"
     ]
    }
   ],
   "source": [
    "# model.stem_sizes = [3,32,32,64]\n",
    "model.stem_sizes = [3,32,64,64]\n",
    "\n",
    "model.act_fn= Mish()\n",
    "model.sa = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## repr model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  model xresnet50\n",
       "  (stem): Sequential(\n",
       "    (conv_0): ConvLayer(\n",
       "      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_1): ConvLayer(\n",
       "      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_2): ConvLayer(\n",
       "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (stem_pool): MaxBlurPool2d()\n",
       "  )\n",
       "  (body): Sequential(\n",
       "    (l_0): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "          (sa): SimpleSelfAttention(\n",
       "            (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_1): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_2): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_4): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_5): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_3): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "    (flat): Flatten()\n",
       "    (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (conv_0): ConvLayer(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_1): ConvLayer(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_2): ConvLayer(\n",
       "    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (stem_pool): MaxBlurPool2d()\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (l_0): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (sa): SimpleSelfAttention(\n",
       "          (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_1): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_2): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_4): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_5): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_3): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (flat): Flatten()\n",
       "  (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lr find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 200 8830"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixup = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_pct = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.070105</td>\n",
       "      <td>2.100344</td>\n",
       "      <td>0.292186</td>\n",
       "      <td>0.815729</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.866643</td>\n",
       "      <td>1.623006</td>\n",
       "      <td>0.508526</td>\n",
       "      <td>0.922881</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.742655</td>\n",
       "      <td>1.805045</td>\n",
       "      <td>0.435480</td>\n",
       "      <td>0.852889</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.639772</td>\n",
       "      <td>1.380168</td>\n",
       "      <td>0.628913</td>\n",
       "      <td>0.942225</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.563969</td>\n",
       "      <td>1.413597</td>\n",
       "      <td>0.611097</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.481639</td>\n",
       "      <td>1.303979</td>\n",
       "      <td>0.665564</td>\n",
       "      <td>0.948842</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.428240</td>\n",
       "      <td>1.295990</td>\n",
       "      <td>0.661237</td>\n",
       "      <td>0.958768</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.374086</td>\n",
       "      <td>1.104493</td>\n",
       "      <td>0.752863</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.352810</td>\n",
       "      <td>1.123726</td>\n",
       "      <td>0.746246</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.297743</td>\n",
       "      <td>1.075232</td>\n",
       "      <td>0.760244</td>\n",
       "      <td>0.967676</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.259686</td>\n",
       "      <td>1.156013</td>\n",
       "      <td>0.728430</td>\n",
       "      <td>0.964368</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.237919</td>\n",
       "      <td>1.015854</td>\n",
       "      <td>0.792314</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.211324</td>\n",
       "      <td>1.066265</td>\n",
       "      <td>0.762535</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.191350</td>\n",
       "      <td>0.978164</td>\n",
       "      <td>0.806058</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.179999</td>\n",
       "      <td>1.058282</td>\n",
       "      <td>0.780606</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.154257</td>\n",
       "      <td>0.982723</td>\n",
       "      <td>0.813948</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.134372</td>\n",
       "      <td>1.079020</td>\n",
       "      <td>0.768134</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.127300</td>\n",
       "      <td>0.943030</td>\n",
       "      <td>0.823874</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.094991</td>\n",
       "      <td>1.041444</td>\n",
       "      <td>0.791041</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.085419</td>\n",
       "      <td>0.916224</td>\n",
       "      <td>0.833036</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.080948</td>\n",
       "      <td>0.988737</td>\n",
       "      <td>0.810384</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.052019</td>\n",
       "      <td>0.930574</td>\n",
       "      <td>0.832018</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.049890</td>\n",
       "      <td>0.955328</td>\n",
       "      <td>0.820056</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.036071</td>\n",
       "      <td>0.917829</td>\n",
       "      <td>0.835582</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.033100</td>\n",
       "      <td>1.065655</td>\n",
       "      <td>0.788241</td>\n",
       "      <td>0.967167</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.012774</td>\n",
       "      <td>0.896183</td>\n",
       "      <td>0.842708</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.003002</td>\n",
       "      <td>0.928197</td>\n",
       "      <td>0.835327</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.990550</td>\n",
       "      <td>0.929567</td>\n",
       "      <td>0.826419</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.990754</td>\n",
       "      <td>0.956265</td>\n",
       "      <td>0.815220</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.991886</td>\n",
       "      <td>0.907947</td>\n",
       "      <td>0.842454</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.982647</td>\n",
       "      <td>0.931135</td>\n",
       "      <td>0.827691</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.976077</td>\n",
       "      <td>0.894184</td>\n",
       "      <td>0.841181</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.958348</td>\n",
       "      <td>0.973580</td>\n",
       "      <td>0.823365</td>\n",
       "      <td>0.971749</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.953947</td>\n",
       "      <td>0.909758</td>\n",
       "      <td>0.837363</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.953223</td>\n",
       "      <td>0.923062</td>\n",
       "      <td>0.828964</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.950491</td>\n",
       "      <td>0.913219</td>\n",
       "      <td>0.840163</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.929046</td>\n",
       "      <td>0.943557</td>\n",
       "      <td>0.829728</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.950688</td>\n",
       "      <td>0.892393</td>\n",
       "      <td>0.846780</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.930737</td>\n",
       "      <td>0.909540</td>\n",
       "      <td>0.845253</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.927967</td>\n",
       "      <td>0.903622</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.922182</td>\n",
       "      <td>0.994662</td>\n",
       "      <td>0.817002</td>\n",
       "      <td>0.965895</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.930749</td>\n",
       "      <td>0.891933</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.926327</td>\n",
       "      <td>0.935927</td>\n",
       "      <td>0.826164</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.915439</td>\n",
       "      <td>0.887290</td>\n",
       "      <td>0.849071</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.907325</td>\n",
       "      <td>0.948130</td>\n",
       "      <td>0.821329</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.914378</td>\n",
       "      <td>0.904465</td>\n",
       "      <td>0.843472</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.903069</td>\n",
       "      <td>0.920557</td>\n",
       "      <td>0.832273</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.911468</td>\n",
       "      <td>0.891379</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.902712</td>\n",
       "      <td>0.955623</td>\n",
       "      <td>0.823365</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.904154</td>\n",
       "      <td>0.880835</td>\n",
       "      <td>0.853907</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.907223</td>\n",
       "      <td>0.934130</td>\n",
       "      <td>0.837872</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.901653</td>\n",
       "      <td>0.891150</td>\n",
       "      <td>0.841435</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.895025</td>\n",
       "      <td>0.925255</td>\n",
       "      <td>0.839145</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.902350</td>\n",
       "      <td>0.900853</td>\n",
       "      <td>0.844744</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.892263</td>\n",
       "      <td>0.898526</td>\n",
       "      <td>0.843217</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.871648</td>\n",
       "      <td>0.892949</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.889639</td>\n",
       "      <td>0.948564</td>\n",
       "      <td>0.826673</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.889902</td>\n",
       "      <td>0.890102</td>\n",
       "      <td>0.847035</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.883976</td>\n",
       "      <td>0.910219</td>\n",
       "      <td>0.837618</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.875464</td>\n",
       "      <td>0.890122</td>\n",
       "      <td>0.850089</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.890042</td>\n",
       "      <td>0.952570</td>\n",
       "      <td>0.825401</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.874782</td>\n",
       "      <td>0.885606</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.867795</td>\n",
       "      <td>0.898183</td>\n",
       "      <td>0.849071</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.870842</td>\n",
       "      <td>0.889495</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.875547</td>\n",
       "      <td>0.905815</td>\n",
       "      <td>0.846526</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.869556</td>\n",
       "      <td>0.888386</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.864473</td>\n",
       "      <td>0.915304</td>\n",
       "      <td>0.840163</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.873391</td>\n",
       "      <td>0.878554</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.864558</td>\n",
       "      <td>0.915392</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.865340</td>\n",
       "      <td>0.893708</td>\n",
       "      <td>0.851362</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.858210</td>\n",
       "      <td>0.923127</td>\n",
       "      <td>0.838890</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.857637</td>\n",
       "      <td>0.894931</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.864259</td>\n",
       "      <td>0.929197</td>\n",
       "      <td>0.841690</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.864979</td>\n",
       "      <td>0.893470</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.862289</td>\n",
       "      <td>0.900325</td>\n",
       "      <td>0.846780</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.860966</td>\n",
       "      <td>0.896711</td>\n",
       "      <td>0.846017</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.850039</td>\n",
       "      <td>0.884862</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.860916</td>\n",
       "      <td>0.889892</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.850746</td>\n",
       "      <td>0.926120</td>\n",
       "      <td>0.833036</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.851714</td>\n",
       "      <td>0.876047</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.846381</td>\n",
       "      <td>0.888825</td>\n",
       "      <td>0.853398</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.846294</td>\n",
       "      <td>0.882673</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.839625</td>\n",
       "      <td>0.905423</td>\n",
       "      <td>0.851362</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.842888</td>\n",
       "      <td>0.871558</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.846686</td>\n",
       "      <td>0.882513</td>\n",
       "      <td>0.857979</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.837195</td>\n",
       "      <td>0.874482</td>\n",
       "      <td>0.855943</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.843520</td>\n",
       "      <td>0.888046</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.845047</td>\n",
       "      <td>0.873981</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.843228</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.857216</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.844642</td>\n",
       "      <td>0.862562</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.838970</td>\n",
       "      <td>0.898304</td>\n",
       "      <td>0.850598</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.846403</td>\n",
       "      <td>0.874089</td>\n",
       "      <td>0.853907</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.837381</td>\n",
       "      <td>0.857065</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.839264</td>\n",
       "      <td>0.858909</td>\n",
       "      <td>0.862815</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.837509</td>\n",
       "      <td>0.897033</td>\n",
       "      <td>0.847544</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.833745</td>\n",
       "      <td>0.880513</td>\n",
       "      <td>0.852889</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.834125</td>\n",
       "      <td>0.876661</td>\n",
       "      <td>0.854416</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.828290</td>\n",
       "      <td>0.886555</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.840632</td>\n",
       "      <td>0.889617</td>\n",
       "      <td>0.850598</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.828420</td>\n",
       "      <td>0.874871</td>\n",
       "      <td>0.856961</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.828391</td>\n",
       "      <td>0.864878</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.834528</td>\n",
       "      <td>0.871012</td>\n",
       "      <td>0.857979</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.824952</td>\n",
       "      <td>0.867814</td>\n",
       "      <td>0.858488</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.820314</td>\n",
       "      <td>0.867461</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.820214</td>\n",
       "      <td>0.859386</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.828096</td>\n",
       "      <td>0.868146</td>\n",
       "      <td>0.861033</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.831799</td>\n",
       "      <td>0.867136</td>\n",
       "      <td>0.862051</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.821954</td>\n",
       "      <td>0.841917</td>\n",
       "      <td>0.872996</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.819290</td>\n",
       "      <td>0.867493</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.813253</td>\n",
       "      <td>0.853228</td>\n",
       "      <td>0.867396</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.816067</td>\n",
       "      <td>0.866721</td>\n",
       "      <td>0.858997</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.815737</td>\n",
       "      <td>0.846737</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.823718</td>\n",
       "      <td>0.859503</td>\n",
       "      <td>0.861797</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.818876</td>\n",
       "      <td>0.849576</td>\n",
       "      <td>0.866124</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.824117</td>\n",
       "      <td>0.860086</td>\n",
       "      <td>0.859506</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.815665</td>\n",
       "      <td>0.859901</td>\n",
       "      <td>0.864597</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.808355</td>\n",
       "      <td>0.854724</td>\n",
       "      <td>0.871214</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.809625</td>\n",
       "      <td>0.852916</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.808583</td>\n",
       "      <td>0.864830</td>\n",
       "      <td>0.864088</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.812331</td>\n",
       "      <td>0.849199</td>\n",
       "      <td>0.865615</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.819108</td>\n",
       "      <td>0.847582</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.820646</td>\n",
       "      <td>0.858355</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.813592</td>\n",
       "      <td>0.861699</td>\n",
       "      <td>0.862051</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.812235</td>\n",
       "      <td>0.843175</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.811289</td>\n",
       "      <td>0.843151</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.810113</td>\n",
       "      <td>0.855262</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.808050</td>\n",
       "      <td>0.847154</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.804508</td>\n",
       "      <td>0.844295</td>\n",
       "      <td>0.871469</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>0.852756</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.816018</td>\n",
       "      <td>0.850436</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.803001</td>\n",
       "      <td>0.843074</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.817699</td>\n",
       "      <td>0.846658</td>\n",
       "      <td>0.871214</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.801071</td>\n",
       "      <td>0.855233</td>\n",
       "      <td>0.864088</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.805798</td>\n",
       "      <td>0.860510</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.799238</td>\n",
       "      <td>0.836042</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.802986</td>\n",
       "      <td>0.827820</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.804419</td>\n",
       "      <td>0.841467</td>\n",
       "      <td>0.872487</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.794934</td>\n",
       "      <td>0.844271</td>\n",
       "      <td>0.869941</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.804450</td>\n",
       "      <td>0.841028</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.807941</td>\n",
       "      <td>0.831275</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.805086</td>\n",
       "      <td>0.848086</td>\n",
       "      <td>0.866124</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.796837</td>\n",
       "      <td>0.837573</td>\n",
       "      <td>0.869687</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.798027</td>\n",
       "      <td>0.837490</td>\n",
       "      <td>0.866124</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.794785</td>\n",
       "      <td>0.832868</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.788836</td>\n",
       "      <td>0.828721</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.808926</td>\n",
       "      <td>0.831351</td>\n",
       "      <td>0.868923</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.791232</td>\n",
       "      <td>0.822424</td>\n",
       "      <td>0.876813</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.789604</td>\n",
       "      <td>0.838039</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.792201</td>\n",
       "      <td>0.824362</td>\n",
       "      <td>0.876813</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.797484</td>\n",
       "      <td>0.817474</td>\n",
       "      <td>0.878341</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.792654</td>\n",
       "      <td>0.831402</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.792766</td>\n",
       "      <td>0.832144</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.787403</td>\n",
       "      <td>0.824150</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.794730</td>\n",
       "      <td>0.824153</td>\n",
       "      <td>0.878341</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.793830</td>\n",
       "      <td>0.822059</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.785818</td>\n",
       "      <td>0.825757</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.784830</td>\n",
       "      <td>0.827725</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.788200</td>\n",
       "      <td>0.824885</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.787154</td>\n",
       "      <td>0.826508</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.795447</td>\n",
       "      <td>0.831856</td>\n",
       "      <td>0.874268</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.797293</td>\n",
       "      <td>0.822663</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.788664</td>\n",
       "      <td>0.824763</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.786787</td>\n",
       "      <td>0.819819</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.788967</td>\n",
       "      <td>0.822966</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.789585</td>\n",
       "      <td>0.823517</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.787990</td>\n",
       "      <td>0.821662</td>\n",
       "      <td>0.879613</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.797424</td>\n",
       "      <td>0.816612</td>\n",
       "      <td>0.879868</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.784692</td>\n",
       "      <td>0.820617</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.780238</td>\n",
       "      <td>0.817882</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.791184</td>\n",
       "      <td>0.818368</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.779600</td>\n",
       "      <td>0.820744</td>\n",
       "      <td>0.879104</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.782468</td>\n",
       "      <td>0.817879</td>\n",
       "      <td>0.879359</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.780332</td>\n",
       "      <td>0.813634</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.789352</td>\n",
       "      <td>0.813723</td>\n",
       "      <td>0.880631</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.785691</td>\n",
       "      <td>0.812310</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.777543</td>\n",
       "      <td>0.817266</td>\n",
       "      <td>0.880631</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.777238</td>\n",
       "      <td>0.811627</td>\n",
       "      <td>0.882667</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.781455</td>\n",
       "      <td>0.813078</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.785329</td>\n",
       "      <td>0.810550</td>\n",
       "      <td>0.881904</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.770402</td>\n",
       "      <td>0.810216</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.774959</td>\n",
       "      <td>0.813443</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.783067</td>\n",
       "      <td>0.810962</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.781457</td>\n",
       "      <td>0.810818</td>\n",
       "      <td>0.883940</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.776338</td>\n",
       "      <td>0.808425</td>\n",
       "      <td>0.884194</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.788341</td>\n",
       "      <td>0.810827</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.783291</td>\n",
       "      <td>0.809485</td>\n",
       "      <td>0.886231</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.779361</td>\n",
       "      <td>0.810821</td>\n",
       "      <td>0.886740</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.781690</td>\n",
       "      <td>0.809110</td>\n",
       "      <td>0.885467</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.769622</td>\n",
       "      <td>0.810100</td>\n",
       "      <td>0.886994</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.786280</td>\n",
       "      <td>0.810480</td>\n",
       "      <td>0.885213</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.777437</td>\n",
       "      <td>0.809076</td>\n",
       "      <td>0.885467</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.780792</td>\n",
       "      <td>0.809122</td>\n",
       "      <td>0.886485</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.782756</td>\n",
       "      <td>0.809557</td>\n",
       "      <td>0.886740</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.783295</td>\n",
       "      <td>0.809766</td>\n",
       "      <td>0.885213</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.780645</td>\n",
       "      <td>0.810742</td>\n",
       "      <td>0.886485</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.785323</td>\n",
       "      <td>0.810355</td>\n",
       "      <td>0.885467</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.782386</td>\n",
       "      <td>0.809509</td>\n",
       "      <td>0.884958</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.783594</td>\n",
       "      <td>0.808672</td>\n",
       "      <td>0.883176</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.783320</td>\n",
       "      <td>0.809324</td>\n",
       "      <td>0.884703</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.776578</td>\n",
       "      <td>0.808978</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0.95, 0.95), 0.2, 0.004)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moms, start_pct, lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.079282</td>\n",
       "      <td>1.964473</td>\n",
       "      <td>0.372614</td>\n",
       "      <td>0.848307</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.878816</td>\n",
       "      <td>1.596639</td>\n",
       "      <td>0.518962</td>\n",
       "      <td>0.913973</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.745503</td>\n",
       "      <td>1.513692</td>\n",
       "      <td>0.559430</td>\n",
       "      <td>0.929244</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.635050</td>\n",
       "      <td>1.378658</td>\n",
       "      <td>0.627132</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.555051</td>\n",
       "      <td>1.452612</td>\n",
       "      <td>0.608806</td>\n",
       "      <td>0.925681</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.481158</td>\n",
       "      <td>1.225280</td>\n",
       "      <td>0.691525</td>\n",
       "      <td>0.961059</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.421006</td>\n",
       "      <td>1.483072</td>\n",
       "      <td>0.571901</td>\n",
       "      <td>0.947060</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.373685</td>\n",
       "      <td>1.133786</td>\n",
       "      <td>0.743446</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.333357</td>\n",
       "      <td>1.228569</td>\n",
       "      <td>0.696615</td>\n",
       "      <td>0.953678</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.299420</td>\n",
       "      <td>1.092069</td>\n",
       "      <td>0.763044</td>\n",
       "      <td>0.968694</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.273042</td>\n",
       "      <td>1.127623</td>\n",
       "      <td>0.739883</td>\n",
       "      <td>0.969712</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.241178</td>\n",
       "      <td>1.017054</td>\n",
       "      <td>0.788496</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.207771</td>\n",
       "      <td>1.072250</td>\n",
       "      <td>0.761771</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.187875</td>\n",
       "      <td>0.997039</td>\n",
       "      <td>0.806567</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.174289</td>\n",
       "      <td>1.023850</td>\n",
       "      <td>0.787223</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.134228</td>\n",
       "      <td>0.974191</td>\n",
       "      <td>0.809366</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.127736</td>\n",
       "      <td>1.063313</td>\n",
       "      <td>0.771698</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.113913</td>\n",
       "      <td>0.961567</td>\n",
       "      <td>0.815984</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.086906</td>\n",
       "      <td>0.955574</td>\n",
       "      <td>0.819547</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.084538</td>\n",
       "      <td>0.946200</td>\n",
       "      <td>0.817765</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.075295</td>\n",
       "      <td>0.960751</td>\n",
       "      <td>0.814966</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.060009</td>\n",
       "      <td>0.913349</td>\n",
       "      <td>0.835073</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.044311</td>\n",
       "      <td>0.981454</td>\n",
       "      <td>0.803512</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.036115</td>\n",
       "      <td>0.922495</td>\n",
       "      <td>0.825655</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.006786</td>\n",
       "      <td>1.005731</td>\n",
       "      <td>0.806821</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.007805</td>\n",
       "      <td>0.912662</td>\n",
       "      <td>0.841945</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.991283</td>\n",
       "      <td>0.997429</td>\n",
       "      <td>0.809875</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.986259</td>\n",
       "      <td>0.908158</td>\n",
       "      <td>0.834563</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.988701</td>\n",
       "      <td>0.962600</td>\n",
       "      <td>0.815984</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.979456</td>\n",
       "      <td>0.925144</td>\n",
       "      <td>0.834309</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.958274</td>\n",
       "      <td>0.982341</td>\n",
       "      <td>0.813693</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.968428</td>\n",
       "      <td>0.925803</td>\n",
       "      <td>0.833800</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.956971</td>\n",
       "      <td>0.938972</td>\n",
       "      <td>0.825655</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.943864</td>\n",
       "      <td>0.907933</td>\n",
       "      <td>0.838127</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.949593</td>\n",
       "      <td>0.915501</td>\n",
       "      <td>0.845253</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.949354</td>\n",
       "      <td>0.893812</td>\n",
       "      <td>0.843726</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.954735</td>\n",
       "      <td>1.007636</td>\n",
       "      <td>0.794604</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.943582</td>\n",
       "      <td>0.925214</td>\n",
       "      <td>0.837618</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.922003</td>\n",
       "      <td>0.890093</td>\n",
       "      <td>0.844235</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.921151</td>\n",
       "      <td>0.891628</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.929865</td>\n",
       "      <td>0.967940</td>\n",
       "      <td>0.814711</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.923271</td>\n",
       "      <td>0.898404</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.914798</td>\n",
       "      <td>0.913648</td>\n",
       "      <td>0.840163</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.919893</td>\n",
       "      <td>0.901127</td>\n",
       "      <td>0.840672</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.909507</td>\n",
       "      <td>0.940709</td>\n",
       "      <td>0.832527</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.905751</td>\n",
       "      <td>0.885012</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.916516</td>\n",
       "      <td>0.961272</td>\n",
       "      <td>0.822601</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.909623</td>\n",
       "      <td>0.903848</td>\n",
       "      <td>0.846017</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.904637</td>\n",
       "      <td>0.951229</td>\n",
       "      <td>0.826673</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.899118</td>\n",
       "      <td>0.888157</td>\n",
       "      <td>0.847035</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.894873</td>\n",
       "      <td>0.923526</td>\n",
       "      <td>0.832527</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.898062</td>\n",
       "      <td>0.909812</td>\n",
       "      <td>0.832782</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.895959</td>\n",
       "      <td>0.932987</td>\n",
       "      <td>0.832018</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.893546</td>\n",
       "      <td>0.905536</td>\n",
       "      <td>0.842199</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.874959</td>\n",
       "      <td>0.934864</td>\n",
       "      <td>0.830491</td>\n",
       "      <td>0.968440</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.880526</td>\n",
       "      <td>0.899137</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.890368</td>\n",
       "      <td>0.902312</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.867879</td>\n",
       "      <td>0.885801</td>\n",
       "      <td>0.847544</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.881791</td>\n",
       "      <td>0.920486</td>\n",
       "      <td>0.841945</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.882813</td>\n",
       "      <td>0.906314</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.877891</td>\n",
       "      <td>0.939752</td>\n",
       "      <td>0.836345</td>\n",
       "      <td>0.968694</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.876248</td>\n",
       "      <td>0.888295</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.875446</td>\n",
       "      <td>0.924251</td>\n",
       "      <td>0.836091</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.883197</td>\n",
       "      <td>0.899542</td>\n",
       "      <td>0.845762</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.866063</td>\n",
       "      <td>0.940044</td>\n",
       "      <td>0.827691</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.863566</td>\n",
       "      <td>0.899352</td>\n",
       "      <td>0.845253</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.856615</td>\n",
       "      <td>0.940240</td>\n",
       "      <td>0.827946</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.871108</td>\n",
       "      <td>0.899405</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.866897</td>\n",
       "      <td>0.929022</td>\n",
       "      <td>0.840926</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.877732</td>\n",
       "      <td>0.879944</td>\n",
       "      <td>0.856197</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.855686</td>\n",
       "      <td>0.914436</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.859950</td>\n",
       "      <td>0.897629</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.866785</td>\n",
       "      <td>0.908567</td>\n",
       "      <td>0.840926</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.856171</td>\n",
       "      <td>0.900879</td>\n",
       "      <td>0.850089</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.863861</td>\n",
       "      <td>0.906015</td>\n",
       "      <td>0.840417</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.859535</td>\n",
       "      <td>0.884980</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.852312</td>\n",
       "      <td>0.914328</td>\n",
       "      <td>0.847035</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.843673</td>\n",
       "      <td>0.898690</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.851683</td>\n",
       "      <td>0.944605</td>\n",
       "      <td>0.830237</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.840891</td>\n",
       "      <td>0.911553</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.842296</td>\n",
       "      <td>0.904333</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.854166</td>\n",
       "      <td>0.898877</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.840404</td>\n",
       "      <td>0.937863</td>\n",
       "      <td>0.836345</td>\n",
       "      <td>0.971749</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.847345</td>\n",
       "      <td>0.873528</td>\n",
       "      <td>0.860779</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.844429</td>\n",
       "      <td>0.912937</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.844774</td>\n",
       "      <td>0.890904</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.843005</td>\n",
       "      <td>0.897695</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.842592</td>\n",
       "      <td>0.880596</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.847989</td>\n",
       "      <td>0.881173</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.850462</td>\n",
       "      <td>0.874223</td>\n",
       "      <td>0.857979</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.845257</td>\n",
       "      <td>0.898998</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.836371</td>\n",
       "      <td>0.889214</td>\n",
       "      <td>0.845762</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.837881</td>\n",
       "      <td>0.897316</td>\n",
       "      <td>0.855179</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.841626</td>\n",
       "      <td>0.869648</td>\n",
       "      <td>0.861033</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.836515</td>\n",
       "      <td>0.889221</td>\n",
       "      <td>0.857216</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.845899</td>\n",
       "      <td>0.880474</td>\n",
       "      <td>0.857216</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.828986</td>\n",
       "      <td>0.894052</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.841259</td>\n",
       "      <td>0.864620</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.838720</td>\n",
       "      <td>0.878878</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.834753</td>\n",
       "      <td>0.872713</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.827539</td>\n",
       "      <td>0.894520</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.829854</td>\n",
       "      <td>0.867278</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.830640</td>\n",
       "      <td>0.893284</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.826921</td>\n",
       "      <td>0.875504</td>\n",
       "      <td>0.861033</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.829075</td>\n",
       "      <td>0.887606</td>\n",
       "      <td>0.851362</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.817367</td>\n",
       "      <td>0.858649</td>\n",
       "      <td>0.860270</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.827679</td>\n",
       "      <td>0.878863</td>\n",
       "      <td>0.854925</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.827107</td>\n",
       "      <td>0.862677</td>\n",
       "      <td>0.863833</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.825577</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.829406</td>\n",
       "      <td>0.869203</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.817557</td>\n",
       "      <td>0.877594</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.821915</td>\n",
       "      <td>0.861141</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.820758</td>\n",
       "      <td>0.873000</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.825142</td>\n",
       "      <td>0.869850</td>\n",
       "      <td>0.860270</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.818092</td>\n",
       "      <td>0.882806</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.969967</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.807767</td>\n",
       "      <td>0.863003</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.805109</td>\n",
       "      <td>0.897795</td>\n",
       "      <td>0.848307</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.814281</td>\n",
       "      <td>0.862449</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.817638</td>\n",
       "      <td>0.878320</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.823816</td>\n",
       "      <td>0.864010</td>\n",
       "      <td>0.861033</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.816447</td>\n",
       "      <td>0.851866</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.814647</td>\n",
       "      <td>0.850144</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.810708</td>\n",
       "      <td>0.848072</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.805859</td>\n",
       "      <td>0.844196</td>\n",
       "      <td>0.869178</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.810323</td>\n",
       "      <td>0.847527</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.796574</td>\n",
       "      <td>0.862857</td>\n",
       "      <td>0.862560</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.815617</td>\n",
       "      <td>0.852765</td>\n",
       "      <td>0.871214</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.814231</td>\n",
       "      <td>0.846089</td>\n",
       "      <td>0.868923</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.815907</td>\n",
       "      <td>0.849920</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.807088</td>\n",
       "      <td>0.869128</td>\n",
       "      <td>0.862560</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.799200</td>\n",
       "      <td>0.861475</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.971749</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.803632</td>\n",
       "      <td>0.854306</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.801229</td>\n",
       "      <td>0.840611</td>\n",
       "      <td>0.872487</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.807746</td>\n",
       "      <td>0.843702</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.806401</td>\n",
       "      <td>0.844397</td>\n",
       "      <td>0.869178</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.797068</td>\n",
       "      <td>0.834194</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.794548</td>\n",
       "      <td>0.845431</td>\n",
       "      <td>0.868414</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.791075</td>\n",
       "      <td>0.839847</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.809602</td>\n",
       "      <td>0.839502</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.802864</td>\n",
       "      <td>0.835761</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.791328</td>\n",
       "      <td>0.834753</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.792941</td>\n",
       "      <td>0.829334</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.799593</td>\n",
       "      <td>0.832128</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.796663</td>\n",
       "      <td>0.825811</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.793914</td>\n",
       "      <td>0.820988</td>\n",
       "      <td>0.878086</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.790689</td>\n",
       "      <td>0.830016</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.794545</td>\n",
       "      <td>0.826017</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.789618</td>\n",
       "      <td>0.828004</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.788752</td>\n",
       "      <td>0.832702</td>\n",
       "      <td>0.874777</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.807361</td>\n",
       "      <td>0.827618</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.793618</td>\n",
       "      <td>0.823222</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.791906</td>\n",
       "      <td>0.817601</td>\n",
       "      <td>0.876813</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.790521</td>\n",
       "      <td>0.822058</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.794907</td>\n",
       "      <td>0.818395</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.787261</td>\n",
       "      <td>0.822841</td>\n",
       "      <td>0.874777</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.790795</td>\n",
       "      <td>0.822225</td>\n",
       "      <td>0.878086</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.789021</td>\n",
       "      <td>0.815709</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.785903</td>\n",
       "      <td>0.816878</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.785873</td>\n",
       "      <td>0.817272</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.787944</td>\n",
       "      <td>0.818487</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.794331</td>\n",
       "      <td>0.824023</td>\n",
       "      <td>0.877577</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.783684</td>\n",
       "      <td>0.820051</td>\n",
       "      <td>0.879613</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.794223</td>\n",
       "      <td>0.821440</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.790412</td>\n",
       "      <td>0.818513</td>\n",
       "      <td>0.878341</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.790193</td>\n",
       "      <td>0.818155</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.796095</td>\n",
       "      <td>0.815613</td>\n",
       "      <td>0.882667</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.788063</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.880377</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.782033</td>\n",
       "      <td>0.815736</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.787362</td>\n",
       "      <td>0.816344</td>\n",
       "      <td>0.879868</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.796808</td>\n",
       "      <td>0.816892</td>\n",
       "      <td>0.881649</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.791549</td>\n",
       "      <td>0.819272</td>\n",
       "      <td>0.881140</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.784996</td>\n",
       "      <td>0.818504</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.787038</td>\n",
       "      <td>0.816828</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.785441</td>\n",
       "      <td>0.818520</td>\n",
       "      <td>0.879359</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.788286</td>\n",
       "      <td>0.815449</td>\n",
       "      <td>0.879613</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.786043</td>\n",
       "      <td>0.815828</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.784209</td>\n",
       "      <td>0.813706</td>\n",
       "      <td>0.879868</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.784519</td>\n",
       "      <td>0.815850</td>\n",
       "      <td>0.881140</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.783606</td>\n",
       "      <td>0.811185</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.783960</td>\n",
       "      <td>0.810791</td>\n",
       "      <td>0.883176</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.786859</td>\n",
       "      <td>0.812085</td>\n",
       "      <td>0.881140</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.779069</td>\n",
       "      <td>0.811734</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.786457</td>\n",
       "      <td>0.811057</td>\n",
       "      <td>0.880377</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.789046</td>\n",
       "      <td>0.809928</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.787846</td>\n",
       "      <td>0.810435</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.781303</td>\n",
       "      <td>0.809522</td>\n",
       "      <td>0.882667</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.787844</td>\n",
       "      <td>0.809226</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.786180</td>\n",
       "      <td>0.810567</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.777158</td>\n",
       "      <td>0.809810</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.775722</td>\n",
       "      <td>0.809455</td>\n",
       "      <td>0.883940</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.780444</td>\n",
       "      <td>0.810720</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.774915</td>\n",
       "      <td>0.811285</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.782620</td>\n",
       "      <td>0.811540</td>\n",
       "      <td>0.881649</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.778397</td>\n",
       "      <td>0.811038</td>\n",
       "      <td>0.881649</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.784640</td>\n",
       "      <td>0.812487</td>\n",
       "      <td>0.881140</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.785386</td>\n",
       "      <td>0.810213</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.776468</td>\n",
       "      <td>0.810309</td>\n",
       "      <td>0.881904</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.786017</td>\n",
       "      <td>0.810783</td>\n",
       "      <td>0.881904</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.776608</td>\n",
       "      <td>0.811038</td>\n",
       "      <td>0.882667</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.787112</td>\n",
       "      <td>0.809036</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixup = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.096950</td>\n",
       "      <td>2.392406</td>\n",
       "      <td>0.271061</td>\n",
       "      <td>0.812420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.879687</td>\n",
       "      <td>1.631927</td>\n",
       "      <td>0.500891</td>\n",
       "      <td>0.905065</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.741946</td>\n",
       "      <td>1.598130</td>\n",
       "      <td>0.521252</td>\n",
       "      <td>0.913209</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.645289</td>\n",
       "      <td>1.369540</td>\n",
       "      <td>0.621278</td>\n",
       "      <td>0.942734</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.568440</td>\n",
       "      <td>1.367858</td>\n",
       "      <td>0.624332</td>\n",
       "      <td>0.947824</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.504454</td>\n",
       "      <td>1.261396</td>\n",
       "      <td>0.678544</td>\n",
       "      <td>0.959277</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.451994</td>\n",
       "      <td>1.208391</td>\n",
       "      <td>0.709086</td>\n",
       "      <td>0.958005</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.425561</td>\n",
       "      <td>1.128314</td>\n",
       "      <td>0.749300</td>\n",
       "      <td>0.966658</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.368695</td>\n",
       "      <td>1.208884</td>\n",
       "      <td>0.707305</td>\n",
       "      <td>0.962077</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.338705</td>\n",
       "      <td>1.100779</td>\n",
       "      <td>0.754645</td>\n",
       "      <td>0.966149</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.307099</td>\n",
       "      <td>1.131010</td>\n",
       "      <td>0.746755</td>\n",
       "      <td>0.970476</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.273279</td>\n",
       "      <td>0.992398</td>\n",
       "      <td>0.801222</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.259999</td>\n",
       "      <td>1.182712</td>\n",
       "      <td>0.711886</td>\n",
       "      <td>0.966149</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.235964</td>\n",
       "      <td>1.008507</td>\n",
       "      <td>0.792314</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.214430</td>\n",
       "      <td>1.051279</td>\n",
       "      <td>0.783915</td>\n",
       "      <td>0.965386</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.188132</td>\n",
       "      <td>0.984633</td>\n",
       "      <td>0.806821</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.156149</td>\n",
       "      <td>1.078248</td>\n",
       "      <td>0.759481</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.154382</td>\n",
       "      <td>0.945729</td>\n",
       "      <td>0.826419</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.123145</td>\n",
       "      <td>1.007149</td>\n",
       "      <td>0.794350</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.124138</td>\n",
       "      <td>0.923066</td>\n",
       "      <td>0.828710</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.112345</td>\n",
       "      <td>1.024434</td>\n",
       "      <td>0.777552</td>\n",
       "      <td>0.970476</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.100335</td>\n",
       "      <td>0.923700</td>\n",
       "      <td>0.835073</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.077606</td>\n",
       "      <td>0.964169</td>\n",
       "      <td>0.817256</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.078901</td>\n",
       "      <td>0.944863</td>\n",
       "      <td>0.822347</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.087872</td>\n",
       "      <td>0.944593</td>\n",
       "      <td>0.818783</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.062609</td>\n",
       "      <td>0.925454</td>\n",
       "      <td>0.829219</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.062411</td>\n",
       "      <td>0.920090</td>\n",
       "      <td>0.834309</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.051101</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.832527</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.047522</td>\n",
       "      <td>0.960724</td>\n",
       "      <td>0.816747</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.020996</td>\n",
       "      <td>0.924703</td>\n",
       "      <td>0.826419</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.022655</td>\n",
       "      <td>0.938160</td>\n",
       "      <td>0.829728</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.013440</td>\n",
       "      <td>0.915726</td>\n",
       "      <td>0.842454</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.021113</td>\n",
       "      <td>0.951682</td>\n",
       "      <td>0.816238</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.002361</td>\n",
       "      <td>0.925249</td>\n",
       "      <td>0.840163</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.999636</td>\n",
       "      <td>0.936729</td>\n",
       "      <td>0.820311</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.006786</td>\n",
       "      <td>0.900375</td>\n",
       "      <td>0.840163</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.991088</td>\n",
       "      <td>0.977698</td>\n",
       "      <td>0.814711</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.984125</td>\n",
       "      <td>0.917354</td>\n",
       "      <td>0.841690</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.984828</td>\n",
       "      <td>0.909220</td>\n",
       "      <td>0.839145</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.993935</td>\n",
       "      <td>0.889653</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.974304</td>\n",
       "      <td>0.941021</td>\n",
       "      <td>0.824892</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.967375</td>\n",
       "      <td>0.898084</td>\n",
       "      <td>0.843726</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.966260</td>\n",
       "      <td>0.910951</td>\n",
       "      <td>0.835073</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.970111</td>\n",
       "      <td>0.897739</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.968148</td>\n",
       "      <td>0.948843</td>\n",
       "      <td>0.816747</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.967439</td>\n",
       "      <td>0.912663</td>\n",
       "      <td>0.840926</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.967593</td>\n",
       "      <td>0.918945</td>\n",
       "      <td>0.833800</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.956626</td>\n",
       "      <td>0.893885</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.957679</td>\n",
       "      <td>0.936554</td>\n",
       "      <td>0.823365</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.939594</td>\n",
       "      <td>0.894765</td>\n",
       "      <td>0.842708</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.959128</td>\n",
       "      <td>0.932506</td>\n",
       "      <td>0.829473</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.938877</td>\n",
       "      <td>0.896225</td>\n",
       "      <td>0.842454</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.959715</td>\n",
       "      <td>0.912139</td>\n",
       "      <td>0.835073</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.946298</td>\n",
       "      <td>0.888124</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.940841</td>\n",
       "      <td>0.928388</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.942237</td>\n",
       "      <td>0.912295</td>\n",
       "      <td>0.837363</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.939211</td>\n",
       "      <td>0.895451</td>\n",
       "      <td>0.843217</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.931371</td>\n",
       "      <td>0.884920</td>\n",
       "      <td>0.853907</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.927839</td>\n",
       "      <td>0.914369</td>\n",
       "      <td>0.842708</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.926758</td>\n",
       "      <td>0.917019</td>\n",
       "      <td>0.844999</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.928393</td>\n",
       "      <td>0.942243</td>\n",
       "      <td>0.830746</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.918126</td>\n",
       "      <td>0.883650</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.918361</td>\n",
       "      <td>0.920776</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.927203</td>\n",
       "      <td>0.882237</td>\n",
       "      <td>0.851362</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.924504</td>\n",
       "      <td>0.943567</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.928461</td>\n",
       "      <td>0.896676</td>\n",
       "      <td>0.844999</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.928083</td>\n",
       "      <td>0.917773</td>\n",
       "      <td>0.839654</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.921444</td>\n",
       "      <td>0.876728</td>\n",
       "      <td>0.852889</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.912145</td>\n",
       "      <td>0.922214</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.916879</td>\n",
       "      <td>0.873277</td>\n",
       "      <td>0.855179</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.900526</td>\n",
       "      <td>0.908957</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.917522</td>\n",
       "      <td>0.879579</td>\n",
       "      <td>0.852634</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.921678</td>\n",
       "      <td>0.906997</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.910564</td>\n",
       "      <td>0.883470</td>\n",
       "      <td>0.852889</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.902692</td>\n",
       "      <td>0.883073</td>\n",
       "      <td>0.850598</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.909483</td>\n",
       "      <td>0.871600</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.911729</td>\n",
       "      <td>0.920426</td>\n",
       "      <td>0.839399</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.903029</td>\n",
       "      <td>0.882120</td>\n",
       "      <td>0.854670</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.914348</td>\n",
       "      <td>0.897368</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.904661</td>\n",
       "      <td>0.884017</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.898584</td>\n",
       "      <td>0.902364</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.904482</td>\n",
       "      <td>0.863783</td>\n",
       "      <td>0.860779</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.907110</td>\n",
       "      <td>0.888761</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.891336</td>\n",
       "      <td>0.863306</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.895617</td>\n",
       "      <td>0.882409</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.894728</td>\n",
       "      <td>0.874194</td>\n",
       "      <td>0.859506</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.894873</td>\n",
       "      <td>0.877424</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.894706</td>\n",
       "      <td>0.874686</td>\n",
       "      <td>0.856961</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.884719</td>\n",
       "      <td>0.883309</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.895116</td>\n",
       "      <td>0.871066</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.877117</td>\n",
       "      <td>0.888837</td>\n",
       "      <td>0.846780</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.880163</td>\n",
       "      <td>0.862469</td>\n",
       "      <td>0.862560</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.893060</td>\n",
       "      <td>0.892333</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.897250</td>\n",
       "      <td>0.870576</td>\n",
       "      <td>0.856707</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.884438</td>\n",
       "      <td>0.884785</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.880672</td>\n",
       "      <td>0.860895</td>\n",
       "      <td>0.865615</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.883349</td>\n",
       "      <td>0.885653</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.882536</td>\n",
       "      <td>0.863474</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.872956</td>\n",
       "      <td>0.874403</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.873019</td>\n",
       "      <td>0.867473</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.878726</td>\n",
       "      <td>0.858760</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.879062</td>\n",
       "      <td>0.864342</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.881259</td>\n",
       "      <td>0.857752</td>\n",
       "      <td>0.862815</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.873657</td>\n",
       "      <td>0.842866</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.875787</td>\n",
       "      <td>0.862256</td>\n",
       "      <td>0.859506</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.864279</td>\n",
       "      <td>0.869012</td>\n",
       "      <td>0.859252</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.864453</td>\n",
       "      <td>0.871957</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.861593</td>\n",
       "      <td>0.849350</td>\n",
       "      <td>0.867905</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.875173</td>\n",
       "      <td>0.848925</td>\n",
       "      <td>0.867396</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.846735</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.873015</td>\n",
       "      <td>0.864713</td>\n",
       "      <td>0.860015</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.873634</td>\n",
       "      <td>0.848681</td>\n",
       "      <td>0.868414</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.863126</td>\n",
       "      <td>0.849079</td>\n",
       "      <td>0.867396</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.859630</td>\n",
       "      <td>0.866367</td>\n",
       "      <td>0.859252</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.862008</td>\n",
       "      <td>0.862057</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.855373</td>\n",
       "      <td>0.846599</td>\n",
       "      <td>0.863833</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.855231</td>\n",
       "      <td>0.842009</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.857881</td>\n",
       "      <td>0.856370</td>\n",
       "      <td>0.860015</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.853382</td>\n",
       "      <td>0.843539</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.853988</td>\n",
       "      <td>0.843174</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.862982</td>\n",
       "      <td>0.847966</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.855951</td>\n",
       "      <td>0.846294</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.869267</td>\n",
       "      <td>0.853571</td>\n",
       "      <td>0.867905</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.856241</td>\n",
       "      <td>0.843054</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.853392</td>\n",
       "      <td>0.850532</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.856748</td>\n",
       "      <td>0.859738</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.861778</td>\n",
       "      <td>0.862154</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.842005</td>\n",
       "      <td>0.849369</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.850725</td>\n",
       "      <td>0.848417</td>\n",
       "      <td>0.869941</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.849444</td>\n",
       "      <td>0.846259</td>\n",
       "      <td>0.868669</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.854200</td>\n",
       "      <td>0.844968</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.853128</td>\n",
       "      <td>0.843003</td>\n",
       "      <td>0.871469</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.850828</td>\n",
       "      <td>0.840479</td>\n",
       "      <td>0.872996</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.840725</td>\n",
       "      <td>0.837341</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.850225</td>\n",
       "      <td>0.844746</td>\n",
       "      <td>0.868669</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.852794</td>\n",
       "      <td>0.831113</td>\n",
       "      <td>0.877832</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.846318</td>\n",
       "      <td>0.852788</td>\n",
       "      <td>0.869687</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.844856</td>\n",
       "      <td>0.835627</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.851970</td>\n",
       "      <td>0.833327</td>\n",
       "      <td>0.872996</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.841908</td>\n",
       "      <td>0.844134</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.847948</td>\n",
       "      <td>0.844844</td>\n",
       "      <td>0.869178</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.843740</td>\n",
       "      <td>0.838338</td>\n",
       "      <td>0.872996</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.848097</td>\n",
       "      <td>0.836563</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.849231</td>\n",
       "      <td>0.832645</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.833309</td>\n",
       "      <td>0.830954</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.836397</td>\n",
       "      <td>0.835034</td>\n",
       "      <td>0.870450</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.838546</td>\n",
       "      <td>0.829997</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.844148</td>\n",
       "      <td>0.826181</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.839970</td>\n",
       "      <td>0.827434</td>\n",
       "      <td>0.877577</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.831176</td>\n",
       "      <td>0.819290</td>\n",
       "      <td>0.879868</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.831424</td>\n",
       "      <td>0.820376</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.837387</td>\n",
       "      <td>0.818427</td>\n",
       "      <td>0.881140</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.827629</td>\n",
       "      <td>0.828023</td>\n",
       "      <td>0.878595</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.830724</td>\n",
       "      <td>0.824093</td>\n",
       "      <td>0.876813</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.830379</td>\n",
       "      <td>0.817332</td>\n",
       "      <td>0.880377</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.827286</td>\n",
       "      <td>0.818748</td>\n",
       "      <td>0.878086</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.830331</td>\n",
       "      <td>0.824012</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.825912</td>\n",
       "      <td>0.819104</td>\n",
       "      <td>0.878086</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.834156</td>\n",
       "      <td>0.817327</td>\n",
       "      <td>0.880377</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.831749</td>\n",
       "      <td>0.814634</td>\n",
       "      <td>0.883685</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.833263</td>\n",
       "      <td>0.817358</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.828851</td>\n",
       "      <td>0.824643</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.832590</td>\n",
       "      <td>0.821807</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.828013</td>\n",
       "      <td>0.818899</td>\n",
       "      <td>0.877577</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.832087</td>\n",
       "      <td>0.826738</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.828786</td>\n",
       "      <td>0.823133</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.824313</td>\n",
       "      <td>0.822278</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.823614</td>\n",
       "      <td>0.818888</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.834093</td>\n",
       "      <td>0.819838</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.832404</td>\n",
       "      <td>0.815504</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.826757</td>\n",
       "      <td>0.808493</td>\n",
       "      <td>0.881649</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.829557</td>\n",
       "      <td>0.809548</td>\n",
       "      <td>0.880377</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.832181</td>\n",
       "      <td>0.814194</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.816586</td>\n",
       "      <td>0.811032</td>\n",
       "      <td>0.879868</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.825797</td>\n",
       "      <td>0.807086</td>\n",
       "      <td>0.883685</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.829291</td>\n",
       "      <td>0.807677</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.818455</td>\n",
       "      <td>0.806041</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.822414</td>\n",
       "      <td>0.803624</td>\n",
       "      <td>0.884703</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.821599</td>\n",
       "      <td>0.805687</td>\n",
       "      <td>0.881140</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.828756</td>\n",
       "      <td>0.806017</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.825022</td>\n",
       "      <td>0.804983</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.821238</td>\n",
       "      <td>0.804120</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.824176</td>\n",
       "      <td>0.804632</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.835222</td>\n",
       "      <td>0.804305</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.829951</td>\n",
       "      <td>0.801922</td>\n",
       "      <td>0.886231</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.822521</td>\n",
       "      <td>0.801968</td>\n",
       "      <td>0.884703</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.819960</td>\n",
       "      <td>0.801278</td>\n",
       "      <td>0.884703</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.824008</td>\n",
       "      <td>0.802355</td>\n",
       "      <td>0.883940</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.821557</td>\n",
       "      <td>0.804366</td>\n",
       "      <td>0.884194</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.820372</td>\n",
       "      <td>0.802090</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.821834</td>\n",
       "      <td>0.801140</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.824697</td>\n",
       "      <td>0.802571</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.820569</td>\n",
       "      <td>0.804063</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.820644</td>\n",
       "      <td>0.801997</td>\n",
       "      <td>0.884958</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.822796</td>\n",
       "      <td>0.802018</td>\n",
       "      <td>0.886231</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.832491</td>\n",
       "      <td>0.803262</td>\n",
       "      <td>0.885213</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.820942</td>\n",
       "      <td>0.801274</td>\n",
       "      <td>0.885722</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.818702</td>\n",
       "      <td>0.801176</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.817966</td>\n",
       "      <td>0.801905</td>\n",
       "      <td>0.885722</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.825679</td>\n",
       "      <td>0.802265</td>\n",
       "      <td>0.883176</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# e200 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.883431, 0.882413, 0.883176])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8830066666666667, 0.00043250150931013)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
